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Private cloud makes its comeback, thanks to AI

CIO Business Intelligence

Private cloud providers may be among the key beneficiaries of today’s generative AI gold rush as, once seemingly passé in favor of public cloud, CIOs are giving private clouds — either on-premises or hosted by a partner — a second look. The excitement and related fears surrounding AI only reinforces the need for private clouds.

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What to Do When AI Fails

O'Reilly on Data

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just

Risk 359
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Why you should care about debugging machine learning models

O'Reilly on Data

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. That’s where model debugging comes in. Interpretable ML models and explainable ML.

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Announcing the 2021 Data Impact Awards

Cloudera

2020 saw us hosting our first ever fully digital Data Impact Awards ceremony, and it certainly was one of the highlights of our year. Please note that use cases could include but are not limited to: risk modeling, sentiment analysis, next best action recommendation, anomaly detection, natural language generation, and more.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.

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The future of casino marketing strategy is digital plus data

BizAcuity

There is a need for a predictive analytics tool that can individually target each customer at right time to drive additional revenue. A predictive model that’s gaining traction in the casino business is Recency-Frequency-Monetary (RFM) model. An AI model can also help address player churn.It

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Automating the Automators: Shift Change in the Robot Factory

O'Reilly on Data

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.